MULTI-LAYER CORRECTIVE CASCADE ARCHITECTURE FOR ON-LINE PREDICTIVE ECHO STATE NETWORKS
Applied Artificial Intelligence
Training Methods and Analysis of Composite, Evolved, On-Line Networks for Time Series Prediction
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Architectural and Markovian factors of echo state networks
Neural Networks
Simple deterministically constructed cycle reservoirs with regular jumps
Neural Computation
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Two recently proposed approaches to recognize temporal patterns have been proposed by Jäger with the so called Echo State Network (ESN) and by Maass with the so called Liquid State Machine (LSM). The ESN approach assumes a sort of “black-box” operability of the networks and claims a broad applicability to several different problems using the same principle. Here we propose a simplified version of ESNs which we call Simple Echo State Network (SESN) which exhibits good results in memory capacity and pattern matching tasks and which allows a better understanding of the capabilities and restrictions of ESNs.